Abstract
This paper presents a novel autonomous air-hockey playing collaborative robot (cobot) that provides human-like gameplay against human opponents. Vision-based Bayesian tracking of the puck and striker are used in an Analytic Hierarchy Process (AHP)-based probabilistic tactical layer for high-speed perception. The tactical layer provides commands for an active control layer that controls the Cartesian position and yaw angle of a custom end effector. The active layer uses optimal control of the cobot’s posture inside the task nullspace. The kinematic redundancy is resolved using a weighted Moore-Penrose pseudo-inversion technique. Experiments with human players show high-speed human-like gameplay with potential applications in the growing field of entertainment robotics.
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Notes
- 1.
A video demonstration of the system is available at: https://www.imperial.ac.uk/robot-intelligence/videos/.
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AlAttar, A., Rouillard, L., Kormushev, P. (2019). Autonomous Air-Hockey Playing Cobot Using Optimal Control and Vision-Based Bayesian Tracking. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11650. Springer, Cham. https://doi.org/10.1007/978-3-030-25332-5_31
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